Natural Language Interfaces to Databases (NLIs) can benefit from the advances in statistical parsing over the last fifteen years or so. However, statistical parsers require training on a massive, labeled corpus, and manually creating such a corpus for each database is prohibitively expensive. To address this quandary, this paper reports on the PRECISE NLI, which uses a statistical parser as a “plug in”. The paper shows how a strong semantic model coupled with “light re-training” enables PRECISE to overcome parser errors, and correctly map from parsed questions to the corresponding SQL queries. We discuss the issues in using statistical parsers to build database-independent NLIs, and report on experimental results with the benchmark ATIS data set where PRECISE achieves 94% accuracy.
CITATION STYLE
Popescu, A. M., Armanasu, A., Etzioni, O., Ko, D., & Yates, A. (2004). Modern natural language interfaces to databases: Composing statistical parsing with semantic tractability. In COLING 2004 - Proceedings of the 20th International Conference on Computational Linguistics. Association for Computational Linguistics (ACL).
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